Comparative evaluation of generative artificial intelligence models for synthetic knee radiograph augmentation in clinical research

Abstract Background In this study, the capability of state-of-the-art generative models to synthesize realistic knee radiographs was evaluated to address dataset scarcity in osteoarthritis (OA) research. Methods Three generative frameworks—Style Generative Adversarial Network3 (StyleGAN3), a stable...

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Autors principals:	Kwangho Chung, Ji-Hoon Nam, Arailym Dosset, Yong-Gon Koh, Jae Min Kim, Paul Shinil Kim, Jin Woo Lee, Kyoung-Mi Park, Hyuck Min Kwon, Kyoung-Tak Kang
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-03-01
Col·lecció:	BMC Medical Imaging
Matèries:	X-ray DCGAN StyleGAN3 CycleGAN Knee
Accés en línia:	https://doi.org/10.1186/s12880-026-02244-z
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